Targeting the undruggable: immunotherapy meets personalized oncology in the genomic era

Abstract

Owing to recent advances in genomic technologies, personalized oncology is poised to fundamentally alter cancer therapy. In this paradigm, the mutational and transcriptional profiles of tumors are assessed, and personalized treatments are designed based on the specific molecular abnormalities relevant to each patient's cancer. To date, such approaches have yielded impressive clinical responses in some patients. However, a major limitation of this strategy has also been revealed: the vast majority of tumor mutations are not targetable by current pharmacological approaches. Immunotherapy offers a promising alternative to exploit tumor mutations as targets for clinical intervention. Mutated proteins can give rise to novel antigens (called neoantigens) that are recognized with high specificity by patient T cells. Indeed, neoantigen-specific T cells have been shown to underlie clinical responses to many standard treatments and immunotherapeutic interventions. Moreover, studies in mouse models targeting neoantigens, and early results from clinical trials, have established proof of concept for personalized immunotherapies targeting next-generation sequencing identified neoantigens. Here, we review basic immunological principles related to T-cell recognition of neoantigens, and we examine recent studies that use genomic data to design personalized immunotherapies. We discuss the opportunities and challenges that lie ahead on the road to improving patient outcomes by incorporating immunotherapy into the paradigm of personalized oncology.

Keywords: T cell; cancer; immunotherapy; neoantigen; next-generation sequencing; personalized oncology.

Figure 1.

Proposed use of NGS data to personalize immunotherapy. The goals of immunotherapy are to either potentiate pre-existing antitumor T-cell responses or initiate antitumor T-cell responses if antitumor immunity is low. The level of pre-existing tumor immunity can be assessed by measuring the level of T-cell markers in RNA-seq data [84]. The total mutation load can be determined using whole-genome or whole-exome sequencing (WGS/WES). (A) Patients with low T cells and low mutation load may benefit most from personalized tumor-associated antigen (TAA)-specific vaccines to activate T cells towards highly expressed TAA, which can be identified using RNA-seq data. (B) Patients with low T cells and a high mutation load may benefit most from neoantigen-specific vaccines. (C) Patients with high T cells but a low mutation load may benefit most from standard ACT, to amplify intratumoral T cells against undefined antigens. (D) Patients with high T cells and high mutation load may benefit most from either checkpoint blockade, to relieve T-cell suppression, or neoantigen-targeted ACT, to amplify the mutation-reactive T-cell response. Combination therapies can also be used.